The proliferation of IoT powered devices and unprecedented leaps in AI technology has created an ideal playing ground for future technology – the digital twins. Not only did Gartner predict digital twin technology to be a trending strategy, but also an IDC report stated that by 2020, 30% of global companies would use the technology to collect data from IoT devices to improve productivity by 25%.

The global digital twin share is projected to grow at CAGR 31.9% between 2020 and 2030. 

The world’s governments are putting digital-first in their economic prosperity, while the big companies invested in global digital transformation are leading by example in exploring and solidifying the digital twin capabilities and potential. 

Let’s understand it in its entirety.

What is digital twin technology?

In simple terms, a digital twin is the virtual counterpart of a physical object, process, service, device. It is an exact virtual replica, a digital avatar of something in the real world, starting from a small device, a jet engine, heavy equipment, a locomotive to entire buildings, and cities. 

What does a digital twin do?

A digital twin runs on an entirely different platform, unlike a desktop environment. A computer program is a digital companion to the real object/ process/ service through sensors and collects data from it. In a simulated version, the digital twin simulates and predicts how its physical twin will behave based on or react to the stimuli, commands, and situations around it and perform its function upon the kind of data generated. It can predict what problems its physical counterpart can run into, based on which performance enhancement can be made to ensure maximum results. A digital twin can be created even before building a physical product.

What are the applications of digital twin technology?

With the growing prowess of cloud, AI/ML, and analytics, digital twin technology in IoT powered devices is fundamentally transformative. 


NASA has been using this technology to simulate space research, repair, and maintenance, to keep information flow between ground level and space, space programming to prepare rovers, shuttles, etc, in case of hostile environments.

Industrial Manufacturing and Production

Digital twin technology helps test products in the production line in hypothetical scenarios, ensuring that correct and resilient designs hit the market, increasing product longevity, facilitating collaboration within floors, monitor products remotely to remove snags, avert breakdown, and reduce customer complaints, thereby increasing revenues.


A virtual twin model of a vehicle and even vehicle components can be prototyped to analyze vehicle/components performance even before relying on the actual vehicle trials. Safety standards can be optimised via accident simulation. Automotive workshops can be aided and trained remotely globally. Maintenance issues can be solved in real-time, and sales in car showrooms can be boosted by allowing customers to try out colors add-ons, make models, etc. 


Digital twin technology in healthcare can help create identical models of a person’s physiology to study a person’s internal organs and decide the impact of a drug, devise restorative therapy, and create personalized medicine to exactly suit a person’s genetic makeup.


From designing structurally sound and safe buildings to cutting down the costs and timelines involved in building a project to simulate the type of materials and components and create computer designs to remove traditional costs in prefabrication, digital twin modeling, and testing can make construction future-facing and less complicated.

Advantages of digital twin technology

With the help of sensor technology, the creation of massive data sets compiled from historical inputs, real-time activities, and predictive ML can generate a goldmine of actionable insights.

Digital twins can generate visibility/transparency in operations, assets, components, systems and machines, thus create reliable business practices and procedures.

Products/services are pre-tested and analyzed; bottlenecks from future scenarios are eliminated fairly enough; thus, they help a company with faster time to market and lower production costs.

Predictive maintenance includes remote configuration and troubleshooting, which leads to reduced maintenance costs overall, and lowers downtime of a product due to failure and glitches.

As a digital twin can be conceptually created in theory without creating an actual physical companion, it can foster tremendous creativity in developers/architects/product designers. Digital twin technology fosters high concept innovations.

Customer satisfaction and loyalty can be improved by a hundred-fold since products enriched by digital twin technology are intuitive, steady, distinctly capable of self-learning, and come with longer lifecycles.

Employing digital twin technology can hugely mitigate the risk factors involved in operations and delivery, predicting future losses in the digital version, thus avoiding long-term risky investments in something likely to go awry. It helps in maintaining brand image/reputation.

They make supply chain logistics more efficient and accurate.

Productivity is expected to increase by 10%, and business processes are seen to be 30% more efficient.

Disadvantages of digital twin technology

The entire technology relies upon the availability of internet connectivity.

The technology is not deployable at once. It can be scaled from smaller to larger integration.

There is a lack of the use of digital twin technology at a large-scale level across domains, sectors, and industries.

No common standardised data collection patterns and practices could result in useless information for a lack of proper analytics.

Skepticism around whether digital twins can be created fast enough for every segment of large, complex enterprises.

The ecosystem around data sharing and technical unification across sectors is a challenge.

Planning deploying digital twin technology consists of great amounts of coordination and physical network establishment across manufacturers, suppliers, external vendors, clients, etc.

As techniques in monitoring, simulating, and leveraging data generation grows, the digital twin technology is set to evolve at a higher rate in the coming years. By 2025, upto 90% of all IIOT platforms will incorporate various forms of digital twin technology as 53% of other industry verticals will adopt the technology in some form by 2028. The future is bright for early investors.